Diego Ferigo

Diego Ferigo embarked on his academic journey by studying Electronic Engineering at the University of Udine (Italy), where he earned his Master of Science (M.Sc.) degree in 2016 with highest honors. During this period, he ventured abroad to the Simon Fraser University of Vancouver (Canada), delving into cutting-edge research having as outcome a thesis titled "Design of an embedded platform for a FMG-Controlled prosthetic hand".

In 2017, Diego transitioned to the Italian Institute of Technology, where he joined the Dynamic Interaction Control group under the guidance of Dr. Francesco Nori. In his capacity as a Robotics Engineer, Diego played a pivotal role in the development and maintenance of advanced software libraries tailored for applications in human-robot interaction, multimodal sensor fusion, and force and motion estimation. His contributions targeted and influenced the evolution of the iCub humanoid robot, both in simulated environments and in real-world settings.

Building upon his foundation in research and engineering, Diego embarked on a split-site Ph.D. program in late 2018, conducted jointly between the University of Manchester and the Artificial Mechanical Intelligence group at the Italian Institute of Technology, led by Dr. Daniele Pucci. His doctoral research focused on leveraging reinforcement learning techniques for enhancing humanoid robot locomotion, showcasing his expertise in robot modeling, hardware-accelerated simulation technology, and innovative approaches to large-scale synthetic data generation. In November 2022, Diego successfully defended his Ph.D. thesis titled "Simulation Architectures for Reinforcement Learning applied to Robotics", marking a significant milestone in his academic journey.

Following the culmination of his doctoral studies, Diego transitioned into a post-doctoral researcher role within the Artificial Mechanical Intelligence group, where he continues to contribute his expertise to various projects. His research interests encompass a wide array of topics, including rigid-body dynamic algorithms, differentiable and hardware-accelerated robot simulations, numerical optimization, and the intersection of robotics and machine learning, reflecting his enduring passion for advancing the frontiers of robotic technology.